Abstract:
Modelling how shocks propagate in supply chains is an increasingly important challenge in economics. Its relevance has been highlighted in recent years by events such as Covid-19 and the Russian invasion of Ukraine. Agent-based models (ABMs) are a promising approach for this problem. However, calibrating them is hard. We show empirically that it is possible to achieve speed ups of over 3 orders of magnitude when calibrating ABMs of supply networks by running them on GPUs and using automatic differentiation, compared to non-differentiable baselines. This opens the door to scaling ABMs to model the whole global supply network.
Citation:
Hamid, S., Moran, J., Mungo, L., Quera-Bofarull, A., & Towers, S. (2025), 'A differentiable model of supply-chain shocks (Version 1)', arXiv, https://doi.org/10.48550/ARXIV.2511.05231